基于兴趣信息深度融合的网络图书资源推荐

doi:10.13229/j.cnki.jdxbgxb.20231025

Abstract

Abstract:

In order to effectively recommend online book resources to users， a recommendation method for online book resources based on deep fusion of interest information is proposed. Firstly， network users are clustered using the K-means clustering algorithm， and the accuracy of user clustering is improved by optimizing the clustering centers. Secondly， the degree of users' interest in book resources is calculated， and candidate recommended resources are determined based on this. Finally， an LSKGCN model is established to deeply fuse the long-term and short-term interests of users， score the book resources， and the online book resources with high scores are recommended to users. The experimental results show that the recommendation results of this method have high category diversity and content diversity.

Key words: interest information, K-means clustering, book resource recommendation, lskgcn model, information fusion

CLC Number:

TP391

Xiao-yu YI,Mian-zhu YI. Network book resource recommendation based on deep fusion of interest information[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3614-3619.

Figures/Tables 5

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图书资源/个	SIM _c
图书资源/个	本文方法	文献［3］方法	文献［4］方法
100	0.864	0.658	0.713
200	0.868	0.659	0.715
300	0.872	0.659	0.717
400	0.877	0.661	0.720
500	0.885	0.662	0.723

Network book resource recommendation based on deep fusion of interest information

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